<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Benjamin Moyer</title><link>https://bmoyer.net/</link><description>Recent content on Benjamin Moyer</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Mon, 17 Aug 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://bmoyer.net/index.xml" rel="self" type="application/rss+xml"/><item><title>Single-station scalar moment estimation</title><link>https://bmoyer.net/work/single-station-moment/</link><pubDate>Mon, 17 Aug 2026 00:00:00 +0000</pubDate><guid>https://bmoyer.net/work/single-station-moment/</guid><description>Magnitude estimation normally requires a network. Where the network is sparse — much of the southern hemisphere, the oceans, and anywhere a monitoring problem is hardest — you may have one station and a few minutes.
This work asks how well a single three-component record can constrain scalar moment, and more importantly how honestly the uncertainty on that estimate can be reported. A deep ensemble produces a predictive distribution rather than a point value, and the estimates are pooled across whatever stations happen to have returned data.</description></item><item><title>Trans-dimensional gravity and magnetic inversion</title><link>https://bmoyer.net/work/gravity-magnetics/</link><pubDate>Mon, 17 Aug 2026 00:00:00 +0000</pubDate><guid>https://bmoyer.net/work/gravity-magnetics/</guid><description>This page is a stub. Fuller writeup, figures, and code links to follow.
Potential-field inversion is badly non-unique: many subsurface density and susceptibility distributions produce the same measurements at the surface. The usual response is to fix a parameterisation in advance and regularise toward it, which produces a tidy answer and hides the question of how much of that answer the data actually supports.
Trans-dimensional Bayesian inversion treats the number of parameters as unknown and infers it alongside everything else, so the resolution of the result is a posterior quantity rather than a modelling choice.</description></item><item><title>Two hundred packets changed my mind</title><link>https://bmoyer.net/notes/two-hundred-packets/</link><pubDate>Mon, 17 Aug 2026 00:00:00 +0000</pubDate><guid>https://bmoyer.net/notes/two-hundred-packets/</guid><description>I was choosing between two VPS locations and did what everyone does: ten pings to each, compare the averages, pick the lower one.
min avg max mdev Buffalo 18.5 20.3 25.2 1.9 Chicago 24.3 26.1 39.1 4.3 Clear enough. Buffalo by 5.8 ms, and Chicago has a 39 ms outlier besides. Except ten samples is nothing, and the outlier was the only hint that the distributions might not be what the means suggested.</description></item><item><title>Single-station moment estimates</title><link>https://bmoyer.net/monitor/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://bmoyer.net/monitor/</guid><description>FISHER — Fast Inference of Seismic moment with Heteroscedastic Error Reporting. One station, one vertical component, one window of P-wave motion, and a magnitude with an uncertainty attached to it. The uncertainty is the reason for the mouthful: it is not a constant, it widens with magnitude and with how hard the record is, and it is calibrated rather than assumed.
Every event above Mw 5.5 with adequate signal is scored automatically, within minutes of the origin, and published here.</description></item></channel></rss>